scholarly journals RECONSTRUCTION OF SEAWATER d18O SIGNAL FROM CORAL d18O: A RECORD FROM BALI CORAL, INDONESIA

2016 ◽  
Vol 25 (1) ◽  
pp. 23
Author(s):  
Sri Yudawati Cahyarini
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Time Series Data ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Series Data ◽  
Surface Salinity ◽  
Decadal Scale ◽  
Long Time

Sea surface salinity (SSS) and precipitation are important climate (paleoclimate) parameters. To obtain long time series data of SSS/precipitation one use coral proxy. In this study, seawater d18O is extracted from d18O content in Bali coral using centering method. The result shows more convincing that d18Obali is influenced by both seawater d18O and sea surface temperature (SST). In the interannual/decadal scale the variation d18Obali clearly shows the variation of seawater d18O, it is supposed that highly variation of precipitation contribute to the seawater d18O variation which mirrored by coral d18Obali. Keywords: coral d18O, seawater d18O, precipitation, sea surface salinity, sea surface temperature Salinitas permukaan laut (SSS) dan curah hujan merupakan parameter penting untuk studi iklim maupun paleoiklim (iklim masa lampau). Untuk mendapatkan data dalam urut-urutan waktu (timeseries) yang panjang dari SSS dan curah hujan diperlukan data proksi geokimia dalam koral. Dalam studi ini kandungan d18O dalam air laut dapat di rekonstruksi dari kandungan d18O dalam koral dengan menggunakan metode centering. Hasilnya menunjukkan bahwa d18O dalam koral dipengaruhi oleh kandungan d18O dalam air laut dan SST. Dalam resolusi tahunan dan puluhan tahunan variasi d18Obali dalam koral menunjukkan dengan jelas variasi d18O dalam air laut, hal ini diduga bahwa dalam resolusi tahunan dan puluhan tahunan variasi curah hujan sangat tinggi yang berkontribusi pada tingginya variasi d18Obali dalam air laut sehingga dapat terekam oleh koral. Kata kunci: d18O koral, d18O air laut, curah hujan, salinitas permukaan laut, suhu permukaan laut.

scholarly journals ANALISIS KORELASI CURAH HUJAN DAN SUHU PERMUKAAN LAUT WILAYAH INDONESIA, SERTA IMPLIKASINYA UNTUK PRAKIRAAN CURAH HUJAN (STUDI KASUS KABUPATEN CILACAP) (CORRELATION ANALYSIS OF RAINFALL AND INDONESIA SEA SURFACE TEMPERATURE, AND ITS ...

Agromet ◽  
2007 ◽  
Vol 21 (2) ◽  
pp. 46 ◽  
Author(s):  
W. Estiningtyas ◽  
F. Ramadhani ◽  
E. Aldrian
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Negative Correlation ◽  
Prediction Models ◽  
Time Series Data ◽  
Sea Surface ◽  
Rainfall Pattern ◽  
Series Data ◽  
Rainfall Prediction

<p>Significant decrease in rainfall caused extreme climate has significant impact on agriculture sector, especialy food crops production. It is one of reason and push developing of rainfall prediction models as anticipate from extreme climate events. Rainfall prediction models develop base on time series data, and then it has been included anomaly aspect, like rainfall prediction model with Kalman filtering method. One of global parameter that has been used as climate anomaly indicator is sea surface temperature. Some of research indicate, there are relationship between sea surface temperature and rainfall. Relationship between Indonesian rainfall and global sea surface temperature has been known, but its relationship with Indonesian’s sea surface temperature not know yet, especialy for rainfall in smaller area like district. So, therefore the research about relationship between rainfall in distric area and Indonesian’s sea surface temperature and it application for rainfall prediction is needed. Based on Indonesian’s sea surface temperature time series data Januari 1982 until Mei 2006 show there are zona of Indonesian’s sea surface temperature (with temperature more than 27,6 0C) dominan in Januari-Mei and moved with specific pattern. Highest value of spasial correlation beetwen Cilacap’s rainfall and Indonesian’s sea surface temperature is 0,30 until 0,50 with different zona of Indonesian’s sea surface temperature. Highest positive correlation happened in March and July. Negative correlation is -0,30 until -0,70 with highest negative correlation in May and June. Model validation resulted correlation coeffcient 85,73%, fits model 20,74%, r2 73,49%, RMSE 20,5% and standart deviation 37,96. Rainfall prediction Januari-Desember 2007 period indicated rainfall pattern is near same with average rainfall pattern, rainfall less than 100/month. The result of this research indicate Indonesian’s sea surface temperature can be used as indicator rainfall condition in distric area, that means rainfall in district area can be predicted based on Indonesian’s sea surface temperature in zona with highest correlation in every month.</p><p>------------------------------------------------------------------</p><p>Penurunan curah hujan yang cukup signifikan akibat iklim ekstrim telah membawa dampak yang cukup signifikan pula pada sektor pertanian, terutama produksi tanaman pangan. Hal ini menjadi salah satu alasan yang mendorong semakin berkembangnya model-model prakiraan hujan sebagai upaya antipasi terhadap kejadian iklim ekstrim. Model prakiraan hujan yang pada awalnya hanya berbasis pada data time series, kini telah berkembang dengan memperhitungkan aspek anomali iklim, seperti model prakiraan hujan dengan metode filter Kalman. Salah satu indikator global yang dapat digunakan sebagai indikator anomali iklim adalah suhu permukaan laut. Dari berbagai hasil penelitian diketahui bahwa suhu permukaan laut ini memiliki keterkaitan dengan kejadian curah hujan. Hubungan curah hujan Indonesia dengan suhu permukaan laut global sudah banyak diketahui, tetapi keterkaitannya dengan suhu permukaan laut wilayah Indonesia belum banyak mendapat perhatian, terutama untuk curah hujan pada cakupan yang lebih sempit seperti kabupaten. Oleh karena itu perlu dilakukan penelitian yang mengkaji hubungan kedua parameter tersebut serta mengaplikasikannya untuk prakiraan curah hujan pada wilayah Kabupaten. Hasil penelitian berdasarkan data suhu permukaan laut wilayah Indonesia rata-rata Januari 1982 hingga Mei 2006 menunjukkan zona dengan suhu lebih dari 27,6 0C yang dominan pada bulan Januari-Mei dan bergerak dengan pola yang cukup jelas. Korelasi spasial antara curah hujan kabupaten Cilacap dengan SPL wilayah Indonesia rata-rata bulan Januari-Desember menunjukkan korelasi positip tertinggi antara 0,30 hingga 0,50 dengan zona SPL yang beragam. Korelasi tertinggi terjadi pada bulan Maret dan Juli. Sedangkan korelasi negatip berkisar antara -0,30 hingga -0,70 dengan korelasi negatip tertinggi pada bulan Mei dan Juni. Validasi model prakiraan hujan menghasilkan nilai koefisien korelasi 85,73%, fits model 20,74%, r2 sebesar 73,49%, RMSE 20,5% dan standar deviasi 37,96. Hasil prakiraan hujan bulanan periode Januari-Desember 2007 mengindikasikan pola curah hujan yang tidak jauh berbeda dengan rata-rata selama 19 tahun (1988-2006) dengan jeluk hujan kurang dari 100 mm/bulan. Hasil penelitian mengindikasikan bahwa SPL wilayah Indonesia dapat digunakan sebagai indikator untuk menunjukkan kondisi curah hujan di suatu wilayah (kabupaten), artinya curah hujan dapat diprediksi berdasarkan perubahan SPL pada zona-zona dengan korelasi yang tertinggi pada setiap bulannya.</p>

Pemodelan dan Peramalan Deret Waktu Studi Kasus: Suhu Permukaan Laut di Selatan Jawa Timur

2017 ◽  
Vol 1 (2) ◽  
pp. 187-199
Author(s):  
Hutomo Atman Maulana ◽  
Muliah Muliah ◽  
Maria Zefaya Sampe ◽  
Farrah Hanifah
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sample Size ◽  
Time Series Data ◽  
Temperature Time Series ◽  
Sea Surface ◽  
Series Data ◽  
Forecast Time ◽  
Temperature Time

The sea surface temperature is one of the important components that can determine the potential of the sea. This research aims to model and forecast time series data of sea surface temperature by using a Box-Jenkins method. Data in this research are the sea surface temperatures in the South of East Java (January 1983-December 2013) with sample size of 372. 360 data will be used for modeling which is from January 1983 to December 2012, and data in 2013 will be used for forecasting. Based on the results of analysis time series, the appropriate models is SARIMA(1,0,0) (1,0,1)12 where can be written as Yt = 0,010039 + 0,734220Yt−1 + 0,014893Yt−12 − (0,734220)(0,014893)Yt−13 + 0,940726et−12 with  MSE of 0.07888096.Keywords: Sea surface temperature, time series, Box-Jenkins method

scholarly journals Sea Surface Temperature Variability in the Florida Keys and Its Relationship to Coral Cover

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
I. M. Soto ◽  
F. E. Muller Karger ◽  
P. Hallock ◽  
C. Hu
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Time Series Data ◽  
Coral Cover ◽  
Monitoring Program ◽  
Florida Keys ◽  
Sea Surface ◽  
Series Data ◽  
Sst Variability

The hypothesis that moderate variability in Sea Surface Temperature (SST) is associated with higher coral cover and slower rates of decline of coral cover within the Florida Keys National Marine Sanctuary (FKNMS) was examined. Synoptic SST time series covering the period 1994–2008 were constructed for the FKNMS with the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer satellite sensors. The SST data were compared with coral-cover time-series data from 36 sites monitored by the Coral Reef and Evaluation Monitoring Program. Sites that experienced moderately high SST variability relative to other sites showed a trend toward higher percentage coral cover in 2008 and relatively slower rates of decline over the 14-year study period. The results suggest that corals at sites that are continuously exposed to moderate variability in temperature are more resilient than corals typically exposed either to low variability or to extremes.

A hybrid CNN-LSTM based model for the prediction of sea surface temperature using time-series satellite data.

2020 ◽  
Author(s):  
Pavan Kumar Jonnakuti ◽  
Udaya Bhaskar Tata Venkata Sai
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Satellite Data ◽  
Time Series Data ◽  
Sea Surface ◽  
Machine Learning Techniques ◽  
Global Ocean ◽  
Series Data

&lt;p&gt;Sea surface temperature (SST) is a key variable of the global ocean, which affects air-sea interaction processes. Forecasts based on statistics and machine learning techniques did not succeed in considering the spatial and temporal relationships of the time series data. Therefore, to achieve precision in SST prediction we propose a deep learning-based model, by which we can produce a more realistic and accurate account of SST &amp;#8216;behavior&amp;#8217; as it focuses both on space and time. Our hybrid CNN-LSTM model uses multiple processing layers to learn hierarchical representations by implementing 3D and 2D convolution neural networks as a method to better understand the spatial features and additionally we use LSTM to examine the temporal sequence of relations in SST time-series satellite data. Widespread studies, based on the historical satellite datasets spanning from 1980 - present time, in Indian Ocean region shows that our proposed deep learning-based CNN-LSTM model is extremely capable for short and mid-term daily SST prediction accurately exclusive based on the error estimates (obtained from LSTM) of the forecasted data sets.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords: Deep Learning, Sea Surface Temperature, CNN, LSTM, Prediction.&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

Meridional Distribution of Surface CO2 along 67°E of the Indian Ocean

2020 ◽  
Author(s):  
Dong-Jin Kang ◽  
Sang-Hwa Choi ◽  
Daeyeon Kim ◽  
Gyeong-Mok Lee
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Seawater ◽  
Surface Salinity ◽  
The North ◽  
The Indian Ocean ◽  
North And South

&lt;p&gt;Surface seawater carbon dioxide was observed from 3 &amp;#176;S to 27 &amp;#176;S along 67 &amp;#176;E of the Indian Ocean in April 2018 and 2019. Partial pressure of CO&lt;sub&gt;2&lt;/sub&gt;(pCO&lt;sub&gt;2&lt;/sub&gt;) in the surface seawater and the atmosphere were observed every two minutes using an underway CO2 measurement system (General Oceanics Model 8050) installed on R/V Isabu. Surface water temperature and salinity were measured as well. The pCO&lt;sub&gt;2&lt;/sub&gt; was measured using Li-7000 NDIR. Standard gases were measured every 8 hours in five classes with concentrations of 0 &amp;#181;atm, 202 &amp;#181;atm, 350 &amp;#181;atm, 447 &amp;#181;atm, and 359.87 &amp;#181;atm. The fCO&lt;sub&gt;2&lt;/sub&gt; of atmosphere remained nearly constant at 387 &amp;#177; 2 &amp;#181;atm, but the surface seawater fCO&lt;sub&gt;2&lt;/sub&gt; peaked at about 3 &amp;#176;S and tended to decrease toward the north and south. The distribution of fCO&lt;sub&gt;2&lt;/sub&gt; in surface seawater according to latitude tends to be very similar to that of sea surface temperature. In order to investigate the factors that control the distribution of fCO&lt;sub&gt;2&lt;/sub&gt; in surface seawater, we analyzed the sea surface temperature, sea surface salinity, and other factors. The effects of salinity are insignificant, and the surface fCO&lt;sub&gt;2&lt;/sub&gt; distribution is mainly controlled by sea surface temperature and other factors that can be represented mainly by biological activity and mixing.&lt;/p&gt;

scholarly journals New insights of <i>p</i>CO<sub>2</sub> variability in the tropical eastern Pacific Ocean using SMOS SSS

2015 ◽  
Vol 12 (6) ◽  
pp. 4595-4625 ◽  
Author(s):  
C. W. Brown ◽  
J. Boutin ◽  
L. Merlivat
Keyword(s):  
High Resolution ◽  
Pacific Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Dissolved Inorganic Carbon ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Oceanic Circulation ◽  
Surface Salinity ◽  
Partial Pressures

Abstract. Complex oceanic circulation and air–sea interaction make the eastern tropical Pacific Ocean (ETPO) a highly variable source of CO2 to the atmosphere. Although the scientific community have amassed 70 000 surface partial-pressure of carbon dioxide (pCO2) datapoints within the ETPO region over the past 25 years, the spatial and temporal resolution of this dataset is insufficient to fully quantify the seasonal to inter-annual variability of the region, a region where pCO2 has been observed to fluctuate by >300 μatm. Upwelling and rainfall events dominate the surface physical and chemical characteristics of the ETPO, with both yielding unique signatures in sea surface temperature and salinity. Thus, we explore the potential of using a statistical description of pCO2 within sea-surface salinity-temperature space. These SSS/SST relationships are based on in-situ SOCAT data collected within the ETPO. This statistical description is then applied to high resolution (0.25°) SMOS sea surface salinity and OSTIA sea surface temperature in order to compute regional pCO2. As a result, we are able to resolve pCO2 at sufficiently high resolution to elucidate the influence various physical processes have on the pCO2 of the surface ETPO. Normalised (to 2014) oceanic pCO2 between July 2010 and June 2014 within the entire ETPO was 41 μatm supersaturated with respect to 2014 atmospheric partial pressures. Values of pCO2 within the ETPO were found to be broadly split between southeast and a northwest regions. The north west, central and South Equatorial Current regions were supersaturated, with wintertime wind jet driven upwelling found to be the first order control on pCO2 values. This contrasts with the southeastern/Gulf of Panama region, where heavy rainfall combined with rapid stratification of the upper water-column act to dilute dissolved inorganic carbon, and yield pCO2 values undersaturated with respect to atmospheric partial pressures of CO2.

scholarly journals Correcting sea surface temperature spurious effects in salinity retrieved from spaceborne L-band Radiometer measurements

2021 ◽  
Author(s):  
Jacqueline Boutin ◽  
Jean-Luc Vergely ◽  
Emmanuel Dinnat ◽  
Philippe Waldteufel ◽  
Francesco D'Amico ◽  
...  
Keyword(s):  
Dielectric Constant ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Dielectric Constants ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
L Band ◽  
Band Radiometer

&lt;p&gt;We derived a new parametrisation for the dielectric constant of the ocean (Boutin et al. 2020). Earlier studies have pointed out systematic differences between Sea Surface Salinity retrieved from L-band radiometric measurements and measured in situ, that depend on Sea Surface Temperature (SST). We investigate how to cope with these differences given existing physically based radiative transfer models. In order to study differences coming from seawater dielectric constant parametrization, we consider the model of Somaraju and Trumpf (2006) (ST) which is built on sound physical bases and close to a single relaxation term Debye equation. While ST model uses fewer empirically adjusted parameters than other dielectric constant models currently used in salinity retrievals, ST dielectric constants are found close to those obtained using the Meissner and Wentz (2012) (MW) model. The ST parametrization is then slightly modified in order to achieve a better fit with seawater dielectric constant inferred from SMOS data. Upgraded dielectric constant model is intermediate between KS and MW models. Systematic differences between SMOS and in situ salinity are reduced to less than +/-0.2 above 0&amp;#176;C and within +/-0.05 between 7 and 28&amp;#176;C. Aquarius salinity becomes closer to in situ salinity, and within +/-0.1. The order of magnitude of remaining differences is very similar to the one achieved with the Aquarius version 5 empirical adjustment of wind model SST dependency. The upgraded parametrization is recommended for use in processing the SMOS data.&amp;#160;&lt;/p&gt;&lt;p&gt;The rationale for this new parametrisation, results obtained with this new parametrisation in recent SMOS reprocessings and comparisons with other parametrisations will be discussed.&lt;/p&gt;&lt;p&gt;Reference:&lt;/p&gt;&lt;p&gt;Boutin, J.,et al. (2020), Correcting Sea Surface Temperature Spurious Effects in Salinity Retrieved From Spaceborne L-Band Radiometer Measurements, IEEE TGRSS, doi:10.1109/tgrs.2020.3030488.&lt;/p&gt;

scholarly journals Comparison of Satellite-Derived Sea Surface Temperature and Sea Surface Salinity Gradients Using the Saildrone California/Baja and North Atlantic Gulf Stream Deployments

2020 ◽  
Vol 12 (11) ◽  
pp. 1839 ◽  
Author(s):  
Jorge Vazquez-Cuervo ◽  
Jose Gomez-Valdes ◽  
Marouan Bouali
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
North Atlantic ◽  
Gulf Stream ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Temporal Sampling ◽  
The North

Validation of satellite-based retrieval of ocean parameters like Sea Surface Temperature (SST) and Sea Surface Salinity (SSS) is commonly done via statistical comparison with in situ measurements. Because in situ observations derived from coastal/tropical moored buoys and Argo floats are only representatives of one specific geographical point, they cannot be used to measure spatial gradients of ocean parameters (i.e., two-dimensional vectors). In this study, we exploit the high temporal sampling of the unmanned surface vehicle (USV) Saildrone (i.e., one measurement per minute) and describe a methodology to compare the magnitude of SST and SSS gradients derived from satellite-based products with those captured by Saildrone. Using two Saildrone campaigns conducted in the California/Baja region in 2018 and in the North Atlantic Gulf Stream in 2019, we compare the magnitude of gradients derived from six different GHRSST Level 4 SST (MUR, OSTIA, CMC, K10, REMSS, and DMI) and two SSS (JPLSMAP, RSS40km) datasets. While results indicate strong consistency between Saildrone- and satellite-based observations of SST and SSS, this is not the case for derived gradients with correlations lower than 0.4 for SST and 0.1 for SSS products.

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